The Cromulent Economics Blog

16 entries from March 2018

March 30, 2018

Your article Estimating the Value of Medal Success in the Olympic Games has now been published in Journal of Sports Economics Volume 19 Issue 3, April 2018 and can be viewed at doi.org/10.1177/1527002515626221

Here is the abstract:

We estimate Canadians’ willingness to pay (WTP) for medals won by Team Canada in the 2010 Winter Olympic Games using data from contingent valuation method (CVM) surveys of nationally representative samples conducted before and after the Games. The results permit an assessment of Own the Podium, a government program designed to increase Canada’s medal count. International prestige and national pride are important determinants of WTP. The results are sensitive to cost and scope, respondents’ beliefs about the effectiveness of the program, as measured by expected medal count. WTP estimates suggest that Own the Podium generated benefits above its cost to a degree unique in the growing literature of sport CVM studies.

Yes, we tested for endogeneity of the medals variable (Table 7). It's not a clean comparison since we sum the gold and other medals variables for the test, but still. Using the attitudinal (Table 2) variables as instruments correction for endogeneity does not affect the value of medals in the pre-Olympic data (but the predicted (i.e., endogeneity corrected) medals coefficient is statistically significant at less (more than?) than p=.10 [t=1.51]). In the post-Olympic data the endogeneity corrected model leads to a WTP for an additional medal of $58 versus $24 in the biased model. The confidence intervals overlap so I don't feel bad about presenting the more conservative, and more realistic, estimates.

March 28, 2018

In the latest issue of the Journal of Economic Perspectives, the journal has gone all-in on YIMBY with a three-paper symposium on housing. The second paper is about changes in rates of homeownership and benefits and costs of homeownership. The first and third papers, though, are both basically making the same point: housing is way too expensive in many productive US cities, mainly because of excessive restrictions on building new housing stock, and allowing more housing supply is the best way to solve the problem.

This is the basic hypothesis behind the YIMBY movement - which stands for "yes in my backyard", in contrast with the more familiar NIMBY movement: "not in my backyard". There are local YIMBY organizations popping up all over the country, especially in the cities with very high housing costs like the Bay Area and New York. YIMBY-ites attend local zoning board meetings and argue for loosening housing restrictions, which puts them at odds with some traditional urban/liberal/environmentalist types who for a long time have believed that excessive development is bad for the economy and bad for the environment.

There's a great argument to be made though that restrictions on high-density urban development are pretty terrible for the environment since they shift people away from living in the city - where people are generally more energy-efficient - and into the suburbs. Ed Glaeser made this point several years ago:

In much of the country, cars are the biggest carbon emitters, and density determines driving. Households in areas with more than 10,000 people per square mile average 687 gallons of gas per year, while households in areas with fewer than 1,000 people per square mile average 1,164 gallons of gas per year.

Glaeser is also the co-author of the first paper in the JEP symposium, which nicely summarizes the potential efficiency gains of increased urban development and the political obstacles:

The available evidence suggests, but does not definitively prove, that the implicit tax on development created by housing regulations is higher in many areas than any reasonable negative externalities associated with new construction. Consequently, there would appear to be welfare gains from reducing these restrictions. But in a democratic system where the rules for building and land use are largely determined by existing homeowners, development projects face a considerable disadvantage, especially since many of the potential beneficiaries of a new project do not have a place to live in the jurisdiction when possibilities for reducing regulation and expanding the supply of housing are debated.

The other article on housing supply reinforces this view. Startling (to me) was the fact that the US spends more than twice as much subsidizing homeowners (more typically non-dense, suburban, middle or upper class) than it does subsidizing renters (more typically dense, urban, middle or lower class).

But remember that the federal government spends far more on subsidies for homeowners than it does on subsidies for renters, this in the form of the mortgage interest deduction ($71 billion), the deduction for real estate taxes ($31.4 billion), and the tax exclusion on capital gains from housing ($24.1 billion). Taken together, these numbers from 2015 totaled more than double the combined costs of support for low-income non-homeowners like Section 8 housing vouchers ($29 billion), the low-income housing tax credit ($7.6 billion), public housing ($6.5 billion), and accelerated depreciation ($4.7 billion), which is a tax benefit for rental apartment owners who use federal low-income tax credits.

[BTW While both of these articles focus on the efficiency losses of housing restrictions and the huge potential productivity gains of eliminating them, the effect of the restrictions on the environment is only mentioned in passing.]

Abstract: The lost recreational use values from the BP/Deepwater Horizon oil spill in the Gulf of Mexico were estimated from cancelled recreational trips to Northwest Florida. The impacts were calculated using the travel cost method for a single site with primary data collected from an online survey conducted after the spill. The data were collected in August and September 2011 with respondents residing in 13 US states that constitute the primary domestic market for coastal tourism to Northwest Florida. The survey gathered information from respondents on their recreational visits, including detailed information on their past trips and the number of trips cancelled to the study region due to the oil spill. The empirical analysis involves the estimation of a random parameter negative binomial count data demand model. Using this model we find significant preference heterogeneity surrounding the effects of the oil spill. Aggregate damages are estimated to be $207 million.

March 06, 2018

President Donald Trump’s administration has been on a deregulatory bender, particularly when it comes to environmental regulations. As of January, the New York Times counted 67 environmental rules on the chopping block under Trump.

This is not one of Trump’s idiosyncrasies, though. His administration is more ham-handed and flagrant about it, but the antipathy it expresses toward federal regulation falls firmly within the GOP mainstream. Republicans have been complaining about “burdensome” and “job-killing” regulations for so long that their opposition to any particular health, safety, or environmental regulation is now just taken for granted.

For instance, why would the Environmental Protection Agency close a program investigating the effects of toxins on children’s health? Is there some evidence that the money is wasted or poorly spent? Why would the EPA allow more unregulated disposal of toxic coal ash? Don’t people in coal regions deserve clean air and water? Is there any reason to think coal ash is currently well-regulated?

These questions barely come up anymore. Republicans oppose regulations because they are regulations; it’s become reflexive, both for the party and for the media the covers them.

As it happens, though, we know something about the costs and benefits of federal regulations. In fact, Trump’s own administration, specifically the (nonpartisan, at least for now) White House Office of Management and Budget (OMB), just released its annual report on that very subject. (Hat tip to E&E.)

The report was released late on a Friday, with Congress out of session and multiple Trump scandals dominating the headlines. A cynical observer might conclude that the administration wanted the report to go unnoticed.

Why might that be? Well, in a nutshell, it shows that the GOP is wrong about regulations as a general matter and wrong about Obama’s regulations specifically. Those regulations had benefits far in excess of their costs, and they had no discernible effect on jobs or economic growth. ...

March 05, 2018

Environmental and Natural Resource Economics is the best-selling text for natural resource economics and environmental economics courses, offering a policy-oriented approach and introducing economic theory and empirical work from the field. Students will leave the course with a global perspective of both environmental and natural resource economics and how they interact. Complemented by a number of case studies showing how underlying economic principles provided the foundation for specific environmental and resource policies, this key text highlights what can be learned from the actual experience. This new, 11th edition includes updated data, a number of new studies and brings a more international focus to the subject. Key features include:

Extensive coverage of the major issues including climate change, air and water pollution, sustainable development, and environmental justice.

Dedicated chapters on a full range of resources including water, land, forests, fisheries, and recyclables.

Introductions to the theory and method of environmental economics including externalities, benefit-cost analysis, valuation methods, and ecosystem goods and services.

Boxed ‘Examples’ and ‘Debates’ throughout the text which highlight global examples and major talking points.

The text is fully supported with end-of-chapter summaries, discussion questions, and self-test exercises in the book and multiple-choice questions, simulations, references, slides, and an instructor’s manual on the Companion Website.

If I taught our upper-level environmental class I would use this book.

From the Resecon listserv via Lynne Lewis:

New to this Edition

In addition to updating the data in the text, tables, and charts, this edition brings a more international focus. It incorporates many new studies, and as noted below, new topics, new figures, new discussion questions, and new examples. Chapters receiving an especially large amount of new material include valuation, energy, water, and climate change.

New or Expanded Topics

Social cost of carbon (Chapter 3)

The 2017 contemporary guidelines on best practice for both contingent valuation and choice experiments (Chapter 4)

Abstract: In this study we compare willingness to pay for a seafood traceability system from contingent behavior demand and contingent valuation referendum vote models using data from a survey of Gulf of Mexico oyster consumers following the BP oil spill in 2010. We estimate a random effects model of oyster demand using contingent behavior data and find that a traceability program increases demand and consumer surplus. We estimate a referendum model for the seafood traceability program using contingent valuation data. We find that welfare estimates from the contingent behavior and contingent valuation methods are convergent valid under certain conditions.

For me, the most interesting part of the paper is our robustness checks on the contingent behavior and contingent valuation data. Here is what we say about the CVM data:

The traceability program increases consumer surplus per meal by $0.95 with a 95% confidence interval of $0.37 to $1.53. The consumer surplus estimates are robust to alternative econometric models such as random effects Poisson and random and fixed effects ordinary least squares models (these results are available upon request). ...

The conditional mean willingness-to-pay per meal ... constrains willingness-to-pay to be positive (Hanemann 1989). The 95% confidence interval is constructed using a bootstrapping procedure (Krinsky and Robb 1986). The willingness-to-pay per meal is $2.69 with a 95% confidence interval of 1.23 to 4.15. ...

We find that differences in the consumer surplus and mean willingness-to-pay estimates are not statistically significant since the 95% confidence intervals overlap. However, this obscures large differences in the point estimates. Mean willingness-to-pay is 183% higher than the consumer surplus estimate from the demand model.

When referendum data exhibits “fat tails” ... welfare measures will be less robust to alternative models relative to textbook data. The conditional mean welfare measure is not robust to alternatives such as the log-linear logit model (median WTP = $0.27 [-$0.03, $0.56] with the 95% confidence interval in brackets) and the Turnbull (Haab and McConnell 1997) nonparametric estimate (WTP = $1.47 [$1.20, $1.74]) which are not sensitive to the tail of the distribution. However, these two estimates also lead to the conclusion of convergent validity with the consumer surplus estimate.

On the other hand, the conditional mean willingness-to-pay estimate is robust to comparison with the Kriström (1990) nonparametric estimate (WTP = $2.65 [$2.23, $3.06]) (Boman, Bostedt, and Kriström 1999). But the confidence interval of the Kriström estimate does not overlap the confidence interval for the consumer surplus estimate. This lack of convergent validity is due to the narrow Kriström confidence interval which is partially an artifact of the smoothing of the data at the upper two bid amounts.

This sensitivity analysis was prompted by referee comments and my experience with two recent contingent valuation studies that, in my opinion, relied too heavily on the Turnbull estimator, especially with "smoothed" due to non-monotonicity (see Whitehead 2017a, 2017b). Smoothing data is equivalent recoding the dependent variable to fit the theory. Typically, recoding the dependent variable to fit the theory is a strong no-no. In the willingness-to-pay context it is OK in order to provide a lower bound willingness-to-pay estimate. But this estimate should not be used for hypothesis testing in the absence of other estimators. Here is the last line of Whitehead (2017b):

With what Haab and McConnell (2002) call “difficult data,” the entire range of nonparametric and parametric WTP estimates should be examined for validity testing and benefit-cost analysis.

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